System and method for using mobility information in heterogeneous networks

ABSTRACT

Disclosed herein are systems and methods related to reducing or making more efficient handovers from one cell to another cell in a communications network. The method includes receiving mobility data for a device being serviced by a first cell, classifying the device based on the mobility data to yield a classification and making a handoff decision when handing off the device from the first cell to a second cell based at least in part on the classification. An example of the mobility data is a speed at which the device is moving.

PRIORITY

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 14/978,751, filed Dec. 22, 2015, the content ofwhich is herein incorporated by reference in its entirety.

BACKGROUND 1. Technical Field

The present disclosure relates to cell assignments in cellular networksand more specifically to a system and method of using mobility dataabout a speed at which a device is moving to make cell handoffassignments.

2. Introduction

Having a mix of large and small cells in Long Term Evolution (LTE) andother networks has the potential for better utilization of scarcewireless spectrum resources. However, when cellular devices connect tocells based on signal strength, the variability in connection qualityand increase in rate of handovers can lead to reduced efficiency. Theresult can make heterogeneous cells into a liability instead of anadvantage. As efforts to inter-operate cellular and Wi-Fi networks leadto consolidated networks, Wi-Fi antennas will add to the diversity ofwireless access points that can be used.

There is a need and potential advantage of having different types ofcells to improve coverage and usage of spectrum, giving rise toheterogeneous networks (“het nets”). There are difficulties in properlyexecuting handovers between different cells in both cases where thecells utilize the same protocol such as LTE or where different cellsutilize different protocols such as an LTE cell abutting or overlappingwith a Wi-Fi cell.

SUMMARY

The following description relates to a number of different examples ofdifferent approaches and systems for handling cell assignments and/orcell handoffs in a cellular environment. The example method relates tomaking handoff decisions. The method includes receiving mobility datafor a device being serviced by a first cell, classifying the devicebased on a mobility state associated with the mobility data to yield aclassification and making a handoff decision when handing off the devicefrom the first cell to a second cell based at least in part on theclassification. The mobility state can be computed from the mobilitydata or estimated from the mobility data. In one example, theclassification can be one of slow, medium and fast speed. Theclassifications or the classes are usually separated by boundaries whichcan be fixed or variable. For example, a classification can be a speedor a rate of movement of 10-30 MPH. The boundary of 10 MPH at the lowend and 30 MPH at the high end can be fixed or variable based on one ormore parameters. The boundaries can be re-set.

When the system makes a handoff decision, each classification caninclude at least one of a preferred cell type and at least oneacceptable cell type. The preferred cell type for a slow speedclassification can include a micro cell type and a small cell type andan acceptable cell type for the slow speed classification includes alarge cell type. The preferred cell type of a medium speedclassification can include the small cell type, and the acceptable celltype for the medium speed classification includes the large cell type.The preferred cell type of a fast classification can include the largecell type, and the acceptable cell type for the fast classificationincludes none.

When making a handoff decision, the device or the system, in oneexample, can only choose a non-preferred cell type when a receivedsignal is stronger than a higher threshold compared to a thresholdrequired for a preferred cell type. A reselection of a cell can betriggered when the first cell no longer provides a sufficient signal andwhen the first cell is of a non-preferred type. Handing off can includehanding off to the second cell when the second cell is of the preferredtype and is suitable.

The classification can include a location in the first cell, potentiallyof a device, and a path traveled across the first cell. Making thehandoff decision can be further based at least in part on a firstspectral efficiency of the first cell and a second spectral efficiencyof the second cell. Making the handoff decision can further be based atleast in part on a bandwidth used by the device and available bandwidthof the second cell.

A system example includes a processor and a computer-readable storagedevice storing instructions which, when executed by the processor, causethe processor to perform certain operations. The operations includereceiving mobility data for a device being serviced by a first cell,classifying the device based on the mobility data to yield aclassification and making a handoff decision when handing off the devicefrom the first cell to a second cell based at least in part on theclassification.

Another example includes a computer-readable storage device storinginstructions which, when executed by a computing device, cause thecomputing device to perform operations including receiving mobility datafor a device being serviced by a first cell, classifying the devicebased on the mobility data to yield a classification and making ahandoff decision when handing off the device from the first cell to asecond cell based at least in part on the classification.

A second example relates to making an initial assignment of a device toa service cell. In this example, a method includes receiving mobilitydata for a device, classifying the device based on a mobility stateassociated with the mobility data to yield a mobility class andassigning the device to a serving cell based at least in part on themobility class. The mobility state can be computed from the mobilitydata or estimated from the mobility data.

When assigning the device to the serving cell, a reselection can betriggered when a first cell no longer provides a sufficient signal andwhen the first cell is of a non-preferred type. Assigning the device tothe serving cell can include assigning the device to a second cell whenthe second cell is of the preferred type and is suitable.

The mobility class can include one of a location of the device in theserving cell and a path traveled across the serving cell. Assigning thedevice to the serving cell can further be based at least in part on aspectral efficiency of the serving cell. Assigning the device to theserving cell can further be based at least in part on a bandwidth usedby the device and available bandwidth of the serving cell. Assigning thedevice to the serving cell can further be based at least in part onmultiple prioritized lists of cells, each list of the multipleprioritized lists of cells including cells of a same type. In oneaspect, cells in a higher priority list are measured more frequentlythan cells on a lower priority list.

In one possible implementation, the mobile device monitors its servingcell as well as the neighbor cells from the list with the highestpriority. If this list (of the highest priority) is of size smaller thata given parameter, the next list is also considered. In another possibleimplementation, the cells with higher priority are measured morefrequently than cells with lower priority. In determining what cell tohandover to, cells from a higher priority neighbor list are alwaysconsidered before those from a lower priority list.

A system aspect of this second example includes a processor and acomputer-readable storage device storing instructions which, whenexecuted by the processor, cause the processor to perform operationsincluding receiving mobility data for a device, classifying the devicebased on a mobility state associated with the mobility data to yield amobility class and assigning the device to a serving cell based at leastin part on the mobility class. A computer-readable storage device aspectof the second example includes a computer-readable storage devicestoring instructions which, when executed by a computing device, causethe computing device to perform operations including receiving mobilitydata for a device, classifying the device based on a mobility stateassociated with the mobility data to yield a mobility class andassigning the device to a serving cell based at least in part on themobility class.

A third example relates to tracking the distribution of load acrosscells. Once a system with device classification and assignment to celltypes is in place, the system can be monitored to track the distributionof users and load level across cells. If the monitoring detects strongimbalances of cell loads, then, in addition to adjusting the power levelof cells, the system disclosed herein makes it possible to dynamicallyadjust the mobility class boundaries, in order to optimize or improvethe spectrum efficiency and capacity of the network.

One example of such a condition is the temporary concentration of slowusers in a certain area. Under such circumstances, reducing the value ofthe class boundaries would allow more users to be classified as moremobile and get preference for being served by larger cells. Anotheroption is to reduce the power of micro cells in the area so that fewerusers are assigned to them.

According to this third example, a method is disclosed for balancingcell loads in a network in which a device is classified into a mobilityclass based on mobility data and the device is assigned to a servingcell based at least in part on the mobility class. The method includesmonitoring a distribution of devices and load levels across a pluralityof cells to yield an analysis. When the analysis indicates an imbalanceof cell loads, the method includes adjusting a boundary of the mobilityclass. When the analysis indicates an imbalance of cell loads, themethod includes adjusting a power level at least one cell of theplurality of cells. When the analysis indicates the imbalance of cellloads, the method further includes performing both of adjusting thepower level at least one cell of the plurality of cells and adjustingthe boundary of the mobility class. Adjusting the boundary of themobility class includes one of increasing an upper speed associated withan upper boundary of the mobility class and decreasing a lower speedassociated with a lower boundary of the mobility class. Performing oneof adjusting a power level at least one cell of the plurality of cellsand adjusting a boundary of the mobility class improves a spectrumefficiency of the network relative to a previous spectral efficiencyprior to the adjusting of one of the power level and the boundary of themobility class. It is noted that in radio transmissions, the data (bits)are composed of control bytes as well as content bytes. The closer adevice is to an antenna or cell tower, the better the spectralefficiency. When there are handoffs or during a handoff process, thereare often more control bytes being transmitted than content bytes.

Performing one of adjusting a power level at least one cell of theplurality of cells and adjusting a boundary of the mobility classimproves a capacity of the network relative to a previous capacity priorto the adjusting of one of the power level and the boundary of themobility class. When assigning the device to serving cell, each mobilityclass includes at least one of a preferred cell type and at least oneacceptable cell type. The preferred cell type for a slow speedclassification includes a micro cell type and a small cell type, and anacceptable cell type for the slow speed classification includes a largecell type. The preferred cell type of a medium speed classificationincludes the small cell type, and the acceptable cell type for themedium speed classification includes the large cell type.

In one example, the device only chooses a non-preferred cell type onlywhen a received signal is stronger than a higher threshold compared to athreshold required for a preferred cell type. When the device isassigned to the serving cell, a reselection is triggered based on one ormore parameters or events. For example, the reselection can be triggeredwhen a first cell no longer provides a sufficient signal and/or when thefirst cell is of a non-preferred type. Assigning the device to theserving cell includes assigning the device to a second cell when thesecond cell is of the preferred type and is suitable. The mobility classcan include one of a location of the device in the serving cell and apath traveled across the serving cell. The device can be assigned to theserving cell based at least in part on multiple prioritized lists ofcells, each list of the multiple prioritized lists of cells includingcells of a same type.

An example method includes receiving mobility data for a device beingserviced by a first cell, wherein the mobility data includes a vectorindicating a motion of the device; and classifying the device based onthe mobility data to yield a classification. When making a reselectionor a handoff decision, a cell type for the classification can include atleast one of a preferred cell type and at least one of an acceptablecell type.

An example of a system according to the third example is a system forbalancing cell loads in a network in which a device is classified into amobility class based on mobility data and the device is assigned to aserving cell based at least in part on the mobility class. The systemincludes a processor and a computer-readable storage device storinginstructions which, when executed by the processor, cause the processorto perform operations including monitoring a distribution of devices andload levels across a plurality of cells to yield an analysis and whenthe analysis indicates an imbalance of cell loads, adjusting a boundaryof the mobility class. The system can be a network infrastructurecomponent or a mobile device like a smartphone or cell phone.

A computer-readable storage device example of this third exampleincludes a non-transitory computer-readable storage device or mediumthat stores instructions which, when executed by a computing device,cause the computing device to balance cell loads in a network in which adevice is classified into a mobility class based on mobility data andthe device is assigned to a serving cell based at least in part on themobility class. The instructions causing the computing device to performoperations including monitoring a distribution of devices and loadlevels across a plurality of cells to yield an analysis and when theanalysis indicates an imbalance of cell loads, adjusting a boundary ofthe mobility class.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system;

FIG. 2 illustrates an example heterogeneous cellular system;

FIG. 3 illustrates another example of a heterogeneous cellular system;

FIG. 4 illustrates a network in which devices move from cell to cell;and

FIG. 5 illustrates a macro cell overlay environment;

FIG. 6 illustrates an example method;

FIG. 7 illustrates another example method;

FIG. 8 illustrates another example method;

FIG. 9 illustrates a method related to updating a mobility class;

FIG. 10 illustrates a method related to selection of cells for mobiledevice;

FIG. 11 illustrates an RRC-connected rule; and

FIG. 12 illustrates a method for updating a neighbor list.

DETAILED DESCRIPTION

Various example of the disclosure are described in detail below. Whilespecific implementations are described, it should be understood thatthis is done for illustration purposes only. Other components andconfigurations may be used without parting from the spirit and scope ofthe disclosure. Moreover, it should be understood that features orconfigurations herein with reference to one example can be implementedin, or combined with, other examples or examples herein. That is, termssuch as “embodiment”, “variation”, “aspect”, “example”, “configuration”,“implementation”, “case”, and any other terms which may connote anembodiment, as used herein to describe specific features orconfigurations, are not intended to limit any of the associated featuresor configurations to a specific or separate embodiment or embodiments,and should not be interpreted to suggest that such features orconfigurations cannot be combined with features or configurationsdescribed with reference to other embodiments, variations, aspects,examples, configurations, implementations, cases, and so forth. In otherwords, features described herein with reference to a specific example(e.g., embodiment, variation, aspect, configuration, implementation,case, etc.) can be combined with features described with reference toanother example. Precisely, one of ordinary skill in the art willreadily recognize that the various embodiments or examples describedherein, and their associated features, can be combined with each other.

The present disclosure addresses concepts related to performing cellassignments and/or cell handoffs in a heterogeneous cellular system. Abrief introductory description of a basic general purpose system orcomputing device in FIG. 1 which can be employed to practice theconcepts, methods, and techniques disclosed is illustrated. A moredetailed description of various approaches to handoffs and cellassignments will then follow.

These variations shall be described herein as the various examples areset forth. The disclosure now turns to FIG. 1. With reference to FIG. 1,an exemplary system and/or computing device 100 includes a processingunit (CPU or processor) 120 and a system bus 110 that couples varioussystem components including the system memory 130 such as read onlymemory (ROM) 140 and random access memory (RAM) 150 to the processor120. The system 100 can include a cache 122 of high-speed memoryconnected directly with, in close proximity to, or integrated as part ofthe processor 120. The system 100 copies data from the memory 130 and/orthe storage device 160 to the cache 122 for quick access by theprocessor 120. In this way, the cache provides a performance boost thatavoids processor 120 delays while waiting for data. These and othermodules can control or be configured to control the processor 120 toperform various operations or actions. Other system memory 130 may beavailable for use as well. The memory 130 can include multiple differenttypes of memory with different performance characteristics. It can beappreciated that the disclosure may operate on a computing device 100with more than one processor 120 or on a group or cluster of computingdevices networked together to provide greater processing capability. Theprocessor 120 can include any general purpose processor and a hardwaremodule or software module, such as module 1 162, module 2 164, andmodule 3 166 stored in storage device 160, configured to control theprocessor 120 as well as a special-purpose processor where softwareinstructions are incorporated into the processor. The processor 120 maybe a self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric. The processor 120 can include multipleprocessors, such as a system having multiple, physically separateprocessors in different sockets, or a system having multiple processorcores on a single physical chip. Similarly, the processor 120 caninclude multiple distributed processors located in multiple separatecomputing devices, but working together such as via a communicationsnetwork. Multiple processors or processor cores can share resources suchas memory 130 or the cache 122, or can operate using independentresources. The processor 120 can include one or more of a state machine,an application specific integrated circuit (ASIC), or a programmablegate array (PGA) including a field PGA.

The system bus 110 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 140 or the like, may provide the basicroutine that helps to transfer information between elements within thecomputing device 100, such as during start-up. The computing device 100further includes storage devices 160 or computer-readable storage mediasuch as a hard disk drive, a magnetic disk drive, an optical disk drive,tape drive, solid-state drive, RAM drive, removable storage devices, aredundant array of inexpensive disks (RAID), hybrid storage device, orthe like. The storage device 160 can include software modules 162, 164,166 for controlling the processor 120. The system 100 can include otherhardware or software modules. The storage device 160 is connected to thesystem bus 110 by a drive interface. The drives and the associatedcomputer-readable storage devices provide nonvolatile storage ofcomputer-readable instructions, data structures, program modules andother data for the computing device 100. In one aspect, a hardwaremodule that performs a particular function includes the softwarecomponent stored in a tangible computer-readable storage device inconnection with the necessary hardware components, such as the processor120, bus 110, display 170, and so forth, to carry out a particularfunction. In another aspect, the system can use a processor andcomputer-readable storage device to store instructions which, whenexecuted by the processor, cause the processor to perform operations, amethod or other specific actions. The basic components and appropriatevariations can be modified depending on the type of device, such aswhether the device 100 is a small, handheld computing device, a desktopcomputer, or a computer server. When the processor 120 executesinstructions to perform “operations”, the processor 120 can perform theoperations directly and/or facilitate, direct, or cooperate with anotherdevice or component to perform the operations.

Although the exemplary example(s) described herein employs the hard disk160, other types of computer-readable storage devices which can storedata that are accessible by a computer, such as magnetic cassettes,flash memory cards, digital versatile disks (DVDs), cartridges, randomaccess memories (RAMs) 150, read-only memory (ROM) 140, a cablecontaining a bit stream and the like, may also be used in the exemplaryoperating environment. Computer-readable storage media,computer-readable storage devices, or computer-readable memory devices,expressly exclude transitory media such as transitory waves, energy,carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with the computing device 100, an inputdevice 190 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 170 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems enable a user to provide multiple types of input to communicatewith the computing device 100. The communications interface 180generally governs and manages the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic hardware depicted may easily be substituted forimproved hardware or firmware arrangements as they are developed.

For clarity of explanation, the illustrative system example is presentedas including individual functional blocks including functional blockslabeled as a “processor” or processor 120. The functions these blocksrepresent may be provided through the use of either shared or dedicatedhardware, including, but not limited to, hardware capable of executingsoftware and hardware, such as a processor 120 that is purpose-built tooperate as an equivalent to software executing on a general purposeprocessor. For example, the functions of one or more processorspresented in FIG. 1 may be provided by a single shared processor ormultiple processors. (Use of the term “processor” should not beconstrued to refer exclusively to hardware capable of executingsoftware.) Illustrative examples may include microprocessor and/ordigital signal processor (DSP) hardware, read-only memory (ROM) 140 forstoring software performing the operations described below, and randomaccess memory (RAM) 150 for storing results. Very large scaleintegration (VLSI) hardware examples, as well as custom VLSI circuitryin combination with a general purpose DSP circuit, may also be provided.

The logical operations of the various examples are implemented as: (1) asequence of computer-implemented steps, operations, or proceduresrunning on a programmable circuit within a general use computer, (2) asequence of computer-implemented steps, operations, or proceduresrunning on a specific-use programmable circuit; and/or (3)interconnected machine modules or program engines within theprogrammable circuits. The system 100 shown in FIG. 1 can practice allor part of the recited methods, can be a part of the recited systems,and/or can operate according to instructions in the recited tangiblecomputer-readable storage devices. Such logical operations can beimplemented as modules configured to control the processor 120 toperform particular functions according to the programming of the module.For example, FIG. 1 illustrates three modules Mod1 162, Mod2 164 andMod3 166 which are modules configured to control the processor 120.These modules may be stored on the storage device 160 and loaded intoRAM 150 or memory 130 at runtime or may be stored in othercomputer-readable memory locations. System 100 may have other modules(not shown in FIG. 1) configured to control the processor 120.

One or more parts of the example computing device 100, up to andincluding the entire computing device 100, can be virtualized. Forexample, a virtual processor can be a software object that executesaccording to a particular instruction set, even when a physicalprocessor of the same type as the virtual processor is unavailable. Avirtualization layer or a virtual “host” can enable virtualizedcomponents of one or more different computing devices or device types bytranslating virtualized operations to actual operations. Ultimately,however, virtualized hardware of every type is implemented or executedby some underlying physical hardware. Thus, a virtualization computelayer can operate on top of a physical compute layer. The virtualizationcompute layer can include one or more of a virtual machine, an overlaynetwork, a hypervisor, virtual switching, and any other virtualizationapplication.

The processor 120 can include all types of processors disclosed herein,including a virtual processor. However, when referring to a virtualprocessor, the processor 120 includes the software components associatedwith executing the virtual processor in a virtualization layer andunderlying hardware necessary to execute the virtualization layer. Thesystem 100 can include a physical or virtual processor 120 that receiveinstructions stored in a computer-readable storage device, which causethe processor 120 to perform certain operations. When referring to avirtual processor 120, the system also includes the underlying physicalhardware executing the virtual processor 120.

Having disclosed some components of a general computing system in FIG.1, the disclosure now turns to FIG. 2, which illustrates an aspect ofthis disclosure. FIG. 2 represents heterogeneous networks 200 which canbe associated with a wireless protocol such as LTE, or differentwireless protocols. As shown in FIG. 2, a macro cell 202 is includedwithin a group of macro cells 224, 226, 228, 230, 232, and 234. Withineach macro cell is a cell tower 204 as well as various other types oftowers or cells. For example, micro cells 206, 212 and 214 operate usingan eNodeB public antenna. A mobile device 218 is shown as being incommunication with the micro cell 206. A femto cell 208 could alsocommunicate with another mobile device 220. Another example is shown ofa building pico cell 210. Also shown in FIG. 2 are other mobilecommunication systems such as vehicles 216 and 222 which arecommunicating with the macro cell 202. FIG. 2 thus provides an exampleof how heterogeneous networks can exist such that mobile devices 218,220, 216, 222, when moving through a series of cells, may transitionfrom one protocol to another and/or from cells that have larger sizessuch as the macro cell 202 into and out of cells with smaller sizes suchas micro cell 206 and vice versa.

This disclosure provides methods and systems that leverage the differenttypes of cells in a heterogeneous wireless network with the goals ofimproving the service level given to users, as well as the efficiencyand capacity of the network. We offer at least three new ideas that canbe integrated into one system to accomplish these goals. The first idearelates to classification of user devices based on their mobility stateinto a mobility class. The next relates to the assignment of userdevices to serving cells based on the mobility class. The third idearelates to a dynamic adjustment of cell power and/or mobility classboundaries.

Most of the procedures described herein can be implemented on one ormore of a user device, in a radio network controller (RNC) which cancoordinate a group of cells, and/or in a network processor (NP) that canperform more central optimization. Different steps in a coordinatedseries of operations can also be performed on different devices. Devicecontrol is distributed in nature but could provide faster reaction tochanges in mobility state, and some of the algorithms fit this mode ofoperation. Using an RNC or NP provides a more centralized decisionpoint, but reaction to changing conditions can be relatively slower.Changes in loading conditions can also influence the mode of operation.For example, if an NP becomes overloaded, more processing can be done atthe device and/or RNC level.

We now provide more details about the first concept of classifying userdevices. A system can classify user devices based on the device'smobility state. The mobility state is a value representing a speed atwhich the user (and hence the device) is moving. The mobility states canbe obtained or estimated using several potential methods. Each deviceand/or a processor anywhere in the network can employ one or more ofthese methods to determine the mobility state. For example, the mobilitystate can be 10 MPH or 70 MPH. The mobility state could also include avector component in which the state includes a parameter related to adirection that the user device is moving. Thus, the mobility state couldinclude values such as (65,10), which could be the speed and the compassheading (10 degrees in this case). The mobility states may also includeother information such as acceleration, altitude, rate ofascent/descent, etc.

The mobility state can be a mobility estimation based on devicecoordinates that are sampled every time unit. The device speed can becomputed based on the change in the coordinates. Simple averaging over atime window or exponential smoothing is one example of an approach thatcan be used to compute the current mobility state. The system can alsoor alternately use smart car technology to convey speed. Devices insidea moving vehicle can have their speed determined by the vehicle'sspeedometer and transmitted to the device or a network processor andthen used to compute the mobility state. For other users, wearabledevices, such as a fitness monitor, can be used.

A mobility estimation based on handover rate or time in cell can be usedto establish a mobility state. The terms “handover,” “handoff,” andtheir variants refer to the process of transferring an ongoing call ordata session of a device from one cell to another cell with minimal orno interruption of service, typically as the device's geographiclocation changes. For example, assume that the device measures the timebetween handoffs. A network processor can estimate the mobility statebased on the cell size and time in cell. This is a relatively lessaccurate method to determine mobility state. However, it can be used asfeedback metric to evaluate the system.

The mobility state can also be determined by a non-mobile declaration bythe user device. This approach requires wireless protocols to allowdevices to declare themselves as stationary or mobile. While a device isstationary, it does not need to perform mobility-related measurements.

Once the mobility state is determined, using one or more of thetechniques disclosed above, the device or a network processor thenclassifies each device into a mobility class. This can be accomplishedin a number of different ways. One example approach is to use anassociation of each mobility class with a range of mobility states. Anexample is shown in Table 1.

TABLE 1 Mobility Classes Class 1 Stationary and slow devices withmobility state lower than 10 (Slow) meters per minute Class 2 Mediumspeed devices with mobility state between 10 and (Medium) 100 meters perminute Class 3 Fast devices with mobility state over 100 meters perminute (Fast)

The speed limits/thresholds are used to classify devices as defined byclass boundaries. Class boundaries in the above example are 10 metersper minute, which defines the boundary between Class 1 (Slow) and Class2 (Medium). Another class boundary in this example is 100 meters perminute which defines the boundary between Class 2 (Medium) and Class 3(Fast). Of course, the structure can include more than three classes andthe particular boundaries can be any parameter or even a group ofparameters. For example, the class could include a speed range as wellas a direction range. If the direction a device is going will lead to amicro cell within a certain amount of time, that could be, coupled withthe speed, a certain class. An example of this approach could be: Class2 (Medium, 12 degree movement which leads to a micro cell within 2minutes). The classification may also be made based on the relativespeed at which the device is approaching or distancing away from a microcell regardless of the direction in which the device is moving.

Given a network with multiple types of cells, differing in size andpower levels, and user devices classified based on mobility, asdescribed above, the disclosure now provide rules to assign devices tocells based on their mobility class.

For illustration and simplicity of explanation, the case of three typesof cells is presented: Large, Small, and Micro cells. Of course, thesystem could employ more than three defined types of cells. Suchdefinitions could include parameters based on one or more of size,geography, elevation, building/infrastructure within a cell, shape,signal strengths, signal range, device usage, numbers of devices withina cell, device mobility within a cell (i.e., does the cell cover afreeway where the average device speed is 65 MPH), foliage and otherobstructions, etc.

The present disclosure deals with setup and management of radio bearersand does not change the setup and control of S-type bearers betweencells and network gateways.

Described herein is the enhancement of two sets of rules that arecritical to the operation of mobile networks: cell selection for RadioResource Control (RRC)-idle devices and handover for RRC-connecteddevices. As background, when a device is turned on, it has to find aqualified serving cell and then go through an authentication procedureand attachment to the network. The mobile device is then communicatingwith a serving cell in an RRC-idle state without acquiring radioresources. In this state, selection and reselection of serving cell isunder the device's control. Once it establishes an RRC connection, thedevice periodically reports its measurements and is being monitored bythe network to assure quality of service. When the quality of servicefrom the current serving cell is not acceptable and/or there is anothercell better suited to serve, the network can request a handover.

The device in the RRC-idle state will operate as follows: The deviceselects and reselects a serving cell based on signal strengthmeasurement called Reference Signal Received Power (RSRP). Specifically,a cell is suitable for serving the device if its RSRP exceeds a minimumthreshold level and, in addition, satisfies certain serviceconditions—such as, being on a network whose service provider is on thedevice's prioritized list and is not barred for service—which do notreally pertain to this disclosure.

Introducing cell types and mobility classes adds a novel dimension tothe cell selection process. The disclosure provides two possible rules:

A first rule is a simple preference rule that specifies a preferenceorder as depicted in Table 2.

TABLE 2 Preference Rule for Mobility Classes Device Mobility ClassPreferred Cell Types Acceptable Cell Types Class 1 - Slow Micro, SmallLarge Class 2 - Medium Small Large Class 3 - Fast Large None

Table 2 is only exemplary and could, of course, have a number ofdifferent parameters therein. For example, a slow mobile device wouldfirst look for a suitable micro or small cell, which could offer Wi-Ficommunication, and only if none is available it would consider a largecell. A fast-moving device may only consider large cells and is notallowed to consider micro or small cells. This latter rule preventsexcessive number of reselections.

A second rule could require an additional parameter RSRP_(diff) which isspecified by the network. In this case, a cell is suitable for a devicebased on a result from the following equation:

RSRP_(meas)>RSRP_(min) +I(MC, CT)×RSRP_(diff),

where RSRP_(min) is the minimum acceptable receive power, RSRP_(meas) isthe measured value, and I(MC, CT)=0 if cell type CT is preferred for auser equipment (UE) mobility class MC, and I(MC, CT)=1 otherwise.

A more general version of this rule is one with a matrix of valuesRSRP_(diff)(MC, CT), and a cell is suitable if:

RSRP_(meas)>RSRP_(min)+RSRP_(diff)(MC, CT).

Under this rule, the device will choose a non-preferred cell type onlyif the received signal is stronger than a higher threshold compared tothe threshold required for a preferred cell type. For both rules, areselection is triggered when the serving cell no longer provides a goodenough signal. In addition, if the current serving cell is of thenon-preferred type, a reselection can occur if a cell of the preferredtype is detected and is suitable.

Another example process relates to the device in an RRC-Connected State.Once a device is connected, and if the rule for RRC-Idle is followed,the device would be served by a cell that fits its mobility class. Atthis point, in most LTE or other protocol networks, the device monitorsits serving cell and listed neighbor cells.

The feature introduced herein is that instead of a simple list ofneighbor cells, there are multiple prioritized lists which can beanalyzed. Each list includes cells of the same type, or could includecells of different types or have characteristics sharing a certainnumber of parameters. In one possible implementation, the devicemonitors its serving cell as well as the neighbor cells from the listwith the highest priority. If this list (of the highest priority) is ofsize smaller than a given parameter, the next list is also considered.In another possible implementation, the cells with higher priority aremeasured more frequently than cells with lower priority. In determiningwhat cell to handover to, cells from a higher priority neighbor list arealways considered before those from a lower priority list.

When a user (and hence the device) changes its mobility class, thechange is detected by the mobility measurement method(s) in use and isincluded in measurement reports to the serving cell (actually a basestation in the cell, commonly referred to as eNodeB). Initially, thedevice continues to be served by the same cell, but rules forreselecting a new serving cell (for RRC-Idle UE) and for handover (forRRC-Connected UE) would now be according to the new mobility class.

For example, if a walking user (medium class), currently served by asmall cell, enters a vehicle, the device will likely move out of itscurrent cell very quickly. In this case, the device will soon beclassified as fast and will now choose the best large cell. By the sametoken, a fast device in a large cell, may enter a building and becomestationary. The now-stationary device would still be served by the largecell but would now look for, and be qualified to be served by a micro orsmall cell.

Once a system with device classification and assignment to cell types isin place, the system can be monitored to track the distribution of usersand load level across cells. If the monitoring detects strong imbalancesof cell loads, then in addition to adjusting the power level of cells,this disclosure makes it possible to dynamically adjust the mobilityclass boundaries, in order to optimize the spectrum efficiency andcapacity of the network.

One example of such a condition is the temporary concentration of slowusers in a certain area. Under such circumstances, reducing the value ofthe class boundaries would allow more users to be classified as moremobile and get preference for being served by larger cells. Anotheroption is to reduce the power of micro cells in the area so that fewerusers are assigned to them.

The adjustment of boundaries can be also performed based on historicaldata. Perhaps during rush hour, the boundaries are adjusted given thehigh volume of movement of devices through a region of cells. However,during certain times and in certain area the traffic is bad and thus thedevices move slowly. Thus, adjusting the boundaries can take intoaccount such actual device movements experienced in the cells.Adjustments can be made for any number of reasons, including theaddition of a new cell or elimination of a cell, extra charges paid by auser that enable an adjustment or to give a device priority, and soforth.

FIG. 3 illustrates an example power level of a serving cell andneighboring cells. For example, feature 320 illustrates a group of cellswhich can be similar to a group of cells shown in FIG. 2. Feature 302illustrates a macro overlay cell which covers a region associated withcell tower 310, which has overlapping micro cells such as micro cell 304serviced by tower 312, micro cell 306 serviced by tower 316 and microcell 308 serviced by tower 318. The principles disclosed herein coverdifferent methods and processes for making cell assignments as well ascell-to-cell handoffs in a heterogeneous network and in such a scenariowhere there is overlap between different types of cells.

FIG. 4 illustrates an example handoff rate. As is shown in the group ofcells 400, various cells such as 414, 416, 418, 420, 422, 424, 426, 428,430, 432, 434, and 436 are shown. Each is serviced by a cell tower orantenna of some sort (not shown). What is represented in FIG. 4 isdifferent speeds of devices moving through various cells. For example,three arrows are shown as feature 406 which can represent a fast movingdevice moving from cell 420 to 422. A single arrow is shown as feature408 which can represent a slow moving device moving from cell 414 to424. Arrow 412 can represent a slow moving device from cell 414, andinto cell 416 for only a brief moment, and then into cell 418. Arrow 412illustrates an example in which two quick handoffs would need to beprocessed as the device moved from cell 414 to 416 and then again soonthereafter from cell 416 to 418. Similarly, feature 410 illustratesmovement of a device transitioning from cell 424 to cell 428 as well asanother transition from cell 428 to 426. Feature 404 illustrates anotherset of arrows representing movement of a device from cell 432 to 434which could include a portion of the movement in cell 426. As can beappreciated, the principles disclosed herein improve the cell assignmentand handoff capabilities in order to minimize the processing necessaryto perform handoffs.

FIG. 5 illustrates another example cell assignment. The system 500 showsa number of different cells including a macro cell 502 having a capacityof 14 Mbps with a current use of 6 Mbps. Device 506 is a smartphone thatis using 5 Mbps. Note that cell 504 which is a microcell having acapacity of 5 Mbps but currently is communicating with 3 Mbps. Thepresent disclosure enables the assignment of cells in order to improvespectral efficiency.

FIG. 6 illustrates an example method. The method can be performed on oneor more of a mobile device and a system or network based processor. Themethod includes receiving mobility data for a device being serviced by afirst cell (602), classifying the device based on a mobility stateassociated with the mobility data to yield a classification (604) andmaking a handoff decision when handing off the device from the firstcell to a second cell based at least in part on the classification(606). The mobility state can be computed from the mobility data orestimated from the mobility data. The boundary that defines a mobilityclass is fixed or variable and when the boundary is variable, theboundary can be re-set.

When making a handoff decision, each classification can include at leastone of a preferred cell type and at least one acceptable cell type. Thepreferred cell type for a slow speed classification can include a microcell type and a small cell type, and an acceptable cell type for theslow speed classification can include a large cell type. The preferredcell type of a medium speed classification can include the small celltype, and the acceptable cell type for the medium speed classificationcan include the large cell type. The preferred cell type of a fastclassification can include the large cell type, and the acceptable celltype for the fast classification can be none. In one aspect, the deviceonly chooses a non-preferred cell type when a received signal isstronger than a higher threshold compared to a threshold required for apreferred cell type.

When making a handoff decision, a reselection can be triggered when thefirst cell no longer provides a sufficient signal and when the firstcell is of a non-preferred type, and wherein making the handoff decisionincludes handing off to the second cell when the second cell is of thepreferred type and is suitable. The classification can include alocation of a device in the first cell and a path traveled across thefirst cell. Making the handoff decision can be further based at least inpart on a first spectral efficiency of the first cell and a secondspectral efficiency of the second cell. Further, making the handoffdecision can be based at least in part on a bandwidth used by the deviceand available bandwidth of the second cell.

FIG. 7 illustrates another example related to the cell assignment aspectof this disclosure. A method includes receiving mobility data for adevice (702), classifying the device based on a mobility stateassociated with the mobility data to yield a mobility class (704) andassigning the device to a serving cell based at least in part on themobility class (706). When assigning the device to serving cell, eachmobility class includes at least one of a preferred cell type and atleast one acceptable cell type. The preferred cell type for a slow speedclassification can include a micro cell type and a small cell type, andan acceptable cell type for the slow speed classification can include alarge cell type. The preferred cell type of a medium speedclassification can include the small cell type, and the acceptable celltype for the medium speed classification can include the large celltype. The preferred cell type of a fast classification can include thelarge cell type, and the acceptable cell type for the fastclassification can be none. In one aspect, the device only chooses anon-preferred cell type when a received signal is stronger than a higherthreshold compared to a threshold required for a preferred cell type.

When assigning the device to the serving cell, a reselection can betriggered when a first cell no longer provides a sufficient signal andwhen the first cell is of a non-preferred type, and wherein assigningthe device to the serving cell includes assigning the device to a secondcell when the second cell is of the preferred type and is suitable.

The mobility class can include a location of the device in the servingcell or a path traveled across the serving cell. Assigning the device tothe serving cell can be further based at least in part on a spectralefficiency of the serving cell. Assigning the device to the serving cellcan be further based at least in part on a bandwidth used by the deviceand available bandwidth of the serving cell. In another aspect,assigning the device to the serving cell can be further based at leastin part on multiple prioritized lists of cells, each list of themultiple prioritized lists of cells including cells of a same type.Cells in a higher priority list are measured more frequently than cellson a lower priority list.

Once a system with device classification and assignment to cell types isin place, the system can be monitored to track the distribution of usersand load level across cells. If the monitoring detects strong imbalancesof cell loads, then in addition to adjusting the power level of cells,the concepts disclosed herein make it possible to dynamically adjust themobility class boundaries in order to optimize or improve the spectralefficiency and capacity of the network.

One example of such a condition is the temporary concentration of slowusers in a certain area. Under such circumstances, reducing the value ofthe class boundaries would allow more users to be classified as moremobile and get preference for being served by larger cells. Anotheroption is to reduce the power of micro cells in the area so that fewerusers are assigned to them.

FIG. 8 illustrates an example method for balancing cell loads in anetwork in which a device is classified into a mobility class based onmobility data and the device is assigned to a serving cell based atleast in part on the mobility class. The method includes monitoring adistribution of devices and load levels across a plurality of cells toyield an analysis (802), and when the analysis indicates an imbalance ofcell loads, adjusting a boundary of the mobility class (804).

When the analysis indicates an imbalance of cell loads, the method caninclude adjusting a power level at least one cell of the plurality ofcells. When the analysis indicates the imbalance of cell loads, themethod further includes performing both of adjusting the power level atleast one cell of the plurality of cells and adjusting the boundary ofthe mobility class. Adjusting the boundary of the mobility class caninclude increasing an upper speed associated with an upper boundary ofthe mobility class and/or decreasing a lower speed associated with alower boundary of the mobility class. Adjusting a power level at leastone cell of the plurality of cells or adjusting a boundary of themobility class can improve or optimize a spectral efficiency of thenetwork relative to a previous spectral efficiency prior to theadjusting of the power level or the boundary of the mobility class.Adjusting a power level at least one cell of the plurality of cells oradjusting a boundary of the mobility class can improve or optimize acapacity of the network relative to a previous capacity prior to theadjusting of the power level or the boundary of the mobility class. Thedevice can be assigned to the serving cell based at least in part onmultiple prioritized lists of cells, each list of the multipleprioritized lists of cells including cells of a same type or at leastsharing a threshold number of characteristics.

FIG. 9 illustrates another method of setting a mobile class and updatingthe mobility class setting. In the first step, the mobile device is in amobility class MC0 (902). The system will perform a mobility measurement(904) to update the mobility state (MS) (906). This measurement canoccur at a fixed or variable measurement interval (908). Next, thesystem determines whether the mobility state is less than a threshold 1(910). If not, the system determines whether the mobility state is lessthan threshold 2 (912). If the mobility state is not less than threshold2, then the system determines that a new mobility class is in a categorysuch as fast and sets a variable MCn=3 (918). If the mobility state isless than threshold 2, then the system determines that the mobilityclass is another category such as slow and sets a variable MCn=2 (916).If the mobility state is less than threshold 1, then the systemdetermines that the new mobility class is in yet another category suchas very slow and sets MCn=1 (914). The flow from each of steps (914),(916) and (918), is to then determine whether MCn=MC0 (920). If not,then the system assigns MC0 to be MCn and reports the change in mobilityclass (922). If MCn does equal MC0, then the flow returns back to thestep of performing the mobility measurement (904) and the processessentially starts again.

In another aspect, the method can include receiving mobility data for adevice being serviced by a first cell, wherein the mobility dataincludes a vector (or other data structure) indicating a motion of thedevice. The mobility data can be independent of one of a data usepattern and a data rate associated with the device. The motion of thedevice can include any data associated with motion of the device. Forexample, the device might be moving vertically and in no particularnorth or south direction. The mobility data can include a simple yes/novalue indicating that the device is in motion. The mobility data caninclude a rate of movement of the device. The mobility data can alsoinclude data characterizing the speed as slow, medium, fast, or inspecific value such as 10 mph, 60 mph, and so forth.

The method includes classifying the device based on the mobility data toyield a classification. When making a reselection or a handoff decision,a cell type for the classification can include at least one of apreferred cell type and at least one of an acceptable cell type. The atleast one of the preferred cell type can include at least one cell froma prioritized list of cells. A mobility state can be computed orestimated from the mobility data. In one aspect, the vector can includea plurality of dimensions. Optionally, one or more of the dimensions caninclude time. The mobility state associated with the mobility data canalso be associated with a rate of movement of the device. Theclassification can be one of a slow speed, a medium speed and a fastspeed.

In another aspect, a boundary associated with the classification can beone of fixed or variable. For example, one or more cells in a networkcan have defined boundaries in terms of geographic size. The boundariescan be fixed or variable, which values can depend on a number offactors, including the classification. In other words, if the device isclassified with a first classification, that can be reported such thatwhen making a reselection or a handoff decision, the boundariesassociated with the cells might be variable. For example, if themobility data yields to a classification of the device as having a fastspeed, then a boundary associated with one or more cells involved in areselection or a classification, can be defined as variable such thatthey can expand in order to overlap with the device either in itscurrent geographic position or an expected or predicted geographicalposition so as to improve the reselection or handoff process. Such avariable use of the boundary can, for example, move up in time thebeginning of communication between the device and a particular cell overwhen the cell would begin exchanging data with the device under a normalboundary size.

When the boundary is variable, it can also be used to balance a loadbetween cells. For example, if a cell reduces its boundary, some devicesat the peripheral edges may find themselves outside the boundary of thatcell and serviced by other cells.

A control plane and/or a user plane can include at least one signalindicator identifying to a controlling node one or more elements of themobility data of the device or the classification of the device.

In one aspect, the preferred cell type for a slow speed classificationincludes a micro cell type and a small cell type, and an acceptable celltype for the slow speed classification includes a large cell type. Thepreferred cell type of a medium speed classification can include a smallcell type, and the acceptable cell type for the medium speedclassification can include a large cell type. In another aspect, thepreferred cell type of a fast classification can include the large celltype, and the acceptable cell type for the fast classification caninclude none. These of course are only examples of particular cell typesbeing associated with particular classifications of the device.

The device in one aspect only chooses a non-preferred cell type when areceived signal is stronger than a higher threshold compared to athreshold required for a preferred cell type. In another aspect, whenmaking a reselection or handoff decision, the reselection is triggeredwhen the first cell no longer provides a sufficient signal or when thefirst cell is of a non-preferred type. Making the handoff decision inthis scenario can include handing off to a second cell when the secondcell is one of the preferred cell type or is suitable.

In another aspect, the classification can include a location of thefirst cell and a path traveled across the first cell. The path traveledacross the first cell can include a predicted path based on the mobilitydata and/or an actual path traveled by the device at least in partthrough the first cell. Thus, the vector indicating a motion of thedevice can include such information that can be utilized to identify thepath that the device has taken or likely will take. Furthermore, mappinginformation, historical information, machine learning information, dataassociated with other devices, and so forth can be utilized inconnection with the mobility data to identify information about theprevious movement of the device as well as a predicted movement or pathof the device. This information can be utilized when making one or moreof the classification of the device as well as the reselection orhandoff decision for the device.

In another aspect, making the handoff decision can further be based atleast in part on a first spectral efficiency of the first cell and asecond spectral efficiency of a second cell. Making the reselection orhandoff decision can further be based at least in part on a bandwidthused by the device and an available bandwidth of a second cell.

It is also important to note that different aspects or examples of thepresent disclosure can be practiced on different devices. For example,in one aspect, the steps that are performed by a mobile device can beclaimed as an example. Thus, the mobile device can perform steps such astransmitting mobility data to a remote device, or performing steps ofclassification for the device based on the mobility data. In areselection or hand over process, the device will also transmit and/orreceive communication information for handling the handoff. Accordingly,one aspect covers all of the various functionality that would beperformed on the device.

In another aspect, the functionality could be performed in the networkat a controller, or server, base station, or other device or combinationof devices. Thus, the steps of receiving mobility.data, classifying thedevice, making or managing handoff decisions, and so forth, could beperformed by one or more network-based components. An aspect of thisdisclosure would be the steps performed by one or more of such devices.These devices may cover multiple devices managed by a single entity andthus would include one or more processors, storage devices, and soforth.

FIG. 10 illustrates a radio resource control idle rule process. First,the system determines that the mobile device is in a radio resourcecontrol-idle state and/or a mobility class I (1002). The method nextdetermines whether there is a suitable cell that is preferred formobility class I (1004). If yes, then the system selects the preferredcell C with the strongest signal (1008). Next, after a set delay (or avariable delay or a delay based on some parameter), the systemdetermines whether the signal from cell C is below a minimum value(1010). Of course, the signal from cell C would also be equal to orbelow a determined value. If no, then the system determines whether thecell C is still preferred for the mobile device (1002). If yes, then theflow returns after a delay (fixed, variable, or even no delay) back tothe determination (1010). It is noted that if the result of thedetermination in step (1010) is that the signal from the cell C is belowa minimum, then the flow proceeds to position A which, as is seen inFIG. 10, returns the process to step (1004) of determining whether thereis a suitable cell that is preferred for mobility class I. Returning tostep (1012), if the cell C is not still preferred for the mobile device,then the process returns again to the determination step (1004).

If the determination from step (1004) is that there is not a suitablecell that is preferred for mobility class I, then the system determineswhether there is a suitable cell that is acceptable for mobility class I(1006). If yes, then the system selects the acceptable cell C with thestrongest signal (1014). If there is not a suitable cell that isacceptable from mobility class I, the system looks for cells on othercarriers (1016) and the process continues to point A and ultimatelyreturns back to the determination step (1004).

FIG. 11 illustrates a handover rule 1100 for radio resource controlconnected device. At any point in time, a connected device is served bya cell C and is in mobility class I (1102). Every measurement interval(1110), the system checks if the signal strength RSRP_meas is above aminimum level RSRP_min (1104). If it is, there is no action until thenext measurement time. If it is not, then the system selects from theneighbor list the cell with the strongest signal modified by a factorRSRP_diff which depends on the mobility class and cell type (1106).After the new cell is so determined, the system performs a handover tothat cell (1108), which now becomes the serving cell. It is noted thatan example algorithm to determine what “suitable” means could beRSRP_MEAS>RSRP_MIN+RSRP_DIFF (MC, CT).

FIG. 12 describes an example process 1200 for modifying the neighborcell list to account for the mobility class. The system recognized anRRC-connected device in mobility class I (1202) and it keeps a list ofneighbor cells that are suitable and candidates to become serving cells.These neighbor cells can be of any type, and with one of the rulesdescribed earlier, each one of these cells is classified as preferred oracceptable. The system also maintains an indicator IND, which is set to0 whenever the device moves to a new serving cell. Every measurementinterval 1110, the system checks if the number of preferred cells in theneighbor list is higher than a threshold TH1 (1204). If it is and IND isequal to 1 (1210), all acceptable cells are removed for the neighborlist (1212). Going back to step 1204, if the number of preferred cellsis not above the threshold, then if IND is equal to 0 (1206), allsuitable acceptable cells are added to the neighbor list (1208).Otherwise, no action is taken, and the system waits for the next check(1202).

Another aspect of this disclosure relates to a method and apparatus formobility-based cell assignment in heterogeneous networks. Prior artrecognized the need and potential advantage of having different types ofcells to improve coverage and usage of spectrum, giving rise toheterogeneous networks (het nets). Furthermore, there are patentsdealing with estimation of user mobility. However, the issue of how tocombine, the two to actually assign users to cells based on mobilityinformation is still open. In addition, there is prior art on howimprove the execution of handovers, but this disclosure addressesreducing the rate of handovers. Several aspects of this disclosureinclude obtaining mobility estimation based on GPS data associated witha device, using smart car technology to convey speed, obtaining amobility estimation based on handover rate or time in cell, a non-mobiledeclaration by a UE.

Next is discussed the assignment of users o cells based on aboveclassification. There can be a set of rules that combine mobility state,cell type, and current cell loads to rank candidate cells for a newconnection. Another rule can be implemented to decide when to performhandover and which cell to move to. An example method for obtaining orestimating the mobility state in a wireless communications network canbe implemented using a control processor to optimize or consider one ormore of the following factors: a power level of the serving cell and theneighboring cells, a distance estimation the serving cell and theneighboring cells, and the ability to handle a cell change from aserving cell to a neighboring cell while calculating the estimate. Thepower level can be of just a neighboring call or of all the cells andcan be viewed in the aggregate or individually with different powerlevels considered for different types of neighboring cells. The distanceestimation can include geographic space of a respective neighboring cell(how big/small is the cell relative to others?), or a distance to theneighboring cell from a current position of the device. The to handle acell change might take into account that a potential neighboring cell isof the same type as the current cell or is of different type (i.e., WiFIversus LTE).

The control processor can be in at least one of the UE in the servingcell. The mobility estimation can be based on the GPS location of theUE. The estimate of speed makes use of smart car technology. Themobility estimation can be based on handover rate or time in cell. Themobility estimation can be based on a non-mobile declaration by UE. Themobility estimation can be based on the ability to determine signalsuppressed information from a serving cell to a neighboring cell. Any ofthese features can be combined with the systems and methods disclosedherein.

In one aspect, a system for obtaining or estimating the mobility statein a wireless communications network can use a control processor tooptimize or consider one or more of the following factors: a power levelof the serving cell and the neighboring cells, a distance estimation ofthe serving cell and the neighboring cells, an ability to handle cellchange from a serving cell to a neighboring cell while calculating theestimate.

In another aspect, a method for classifying a UE in a wirelesscommunications network is disclosed for using control processor tooptimize the following factors: a power level of the serving cell andthe neighboring cells, a distance estimation of the serving cell and theneighboring cells and/or an ability to handle cell change from a servingcell to a neighboring cell while calculating the estimate.

In yet another example, a system is disclosed for classifying userequipment (UE) in a wireless communications network using a controlprocessor to optimize the following factors: a power level of theserving cell and the neighboring cells, a distance. estimation of theserving cell and the neighboring cells and/or an ability to handlechange. from a serving cell to a neighboring cell while calculating theestimate.

Examples within the scope of the present disclosure may also includetangible and/or non-transitory computer-readable storage devices forcarrying or having computer-executable instructions or data structuresstored thereon. Such tangible computer-readable storage devices can beany available device that can be accessed by a general purpose orspecial purpose computer, including the functional design of any specialpurpose processor as described above. By way of example, and notlimitation, such tangible computer-readable devices can include RAM,ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storageor other magnetic storage devices, or any other device which can be usedto carry or store desired program code in the form ofcomputer-executable instructions, data structures, or processor chipdesign. When information or instructions are provided via a network oranother communications connection (either hardwired, wireless, orcombination thereof) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such connection isproperly termed a computer-readable medium. Combinations of the aboveshould also be included within the scope of the computer-readablestorage devices.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,components, data structures, objects, and the functions inherent in thedesign of special-purpose processors, etc. that perform particular tasksor implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Other examples of the disclosure may be practiced in network computingenvironments with many types of computer system configurations,including personal computers, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, and the like. Examplesmay also be practiced in distributed computing environments where tasksare performed by local and remote processing devices that are linked(either by hardwired links, wireless links, or by a combination thereof)through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

In another example, a method can include receiving mobility data for adevice being serviced by a first cell, wherein the mobility dataincludes a vector indicating a motion of the device and classifying thedevice based on the mobility data to yield a classification, wherein,when making a reselection or a handoff decision, a cell type for theclassification includes at least one of a preferred cell type and atleast one of an acceptable cell type.

In another example, a method can include receiving mobility data in avirtual layer for a device being serviced by a first cell in aheterogeneous network including at least a non-cellular node type and acellular node type, and classifying the mobility data in the virtuallayer for the device to yield a classification, wherein theclassification includes an access type and an indication of an accesspriority to the access type, wherein the access priority includes atleast an acceptable access type or a preferred access type, and whereinthe access type is identified from a list of access types which includesat least one of a non-cellular node type and a cellular node type

The method further includes measuring traffic characteristics from aprioritized list of traffic characteristics, the traffic characteristicsincluding, for a plurality of devices operating within the heterogeneousnetwork, the plurality of devices including the device and at least oneadditional device, two or more of (1) an adjustment of a node boundaryof a node in the heterogenous network, (2) an adjustment of a powerlevel of a node in the heterogenous network, (3) balancing of a cellload of a node in the heterogenous network, (4), a distribution of aload amongst nodes in the heterogenous network based on communicationswith the plurality of devices, (5) a distribution of the plurality ofdevices in the heterogenous network, and (6) a distribution of theplurality of devices and a load level of respective nodes in theheterogenous network, to yield a measurement, and making a handoffdecision in the virtual layer when handing off the device from the firstcell to a second cell based at least in part on the classification andthe measurement.

A mobility state can be computed or estimated from the mobility data.The vector can include a plurality of dimensions, and wherein at leastone of the plurality of dimensions is time. A mobility state associatedwith the mobility data can be a rate of movement of the device. Theclassification also can be one or more of a slow speed, a medium speedand a fast speed. A boundary associated with the classification can beone of fixed or variable. The boundary can be variable and can be usedto balance a load between cells.

In another aspect, a control plane can include at least one signalindicator identifying to a controlling node one or more elements of themobility data of the device or the classification of the device. Thepreferred cell type for a slow speed classification can include a microcell type and a small cell type, and an acceptable cell type for theslow speed classification includes a large cell type. The preferred celltype of a medium speed classification can include a small cell type, andthe acceptable cell type for the medium speed classification includes alarge cell type. The preferred cell type of a fast classification caninclude the large cell type, and the acceptable cell type for the fastclassification includes none.

The device may only choose a non-preferred cell type when a receivedsignal is stronger than a higher threshold compared to a thresholdrequired for a preferred cell type. When making a handoff decision, areselection can be triggered when the first cell no longer provides asufficient signal or when the first cell is of a non-preferred type, andwherein making the handoff decision includes handing off to a secondcell when the second cell is one of the preferred cell type or issuitable. In another aspect, the classification can include a locationof the first cell and a path traveled across the first cell. Making thehandoff decision an further be based at least in part on a firstspectral efficiency of the first cell and a second spectral efficiencyof a second cell. Making the reselection or handoff decision can furtherbe based at least in part on a bandwidth used by the device and anavailable bandwidth of a second cell. The mobility data an also beindependent of one of a data use pattern and a data rate. The at leastone of the preferred cell type can include at least one cell from aprioritized list of cells. Any of the concepts disclosed above in anyexample can be combined with any other concept to yield an exampleconfiguration or process that can be claimed.

A system example can include one or more processors and acomputer-readable storage device storing instructions which, whenexecuted by the processor, cause the one or more processors to performoperations including: receiving mobility data for a device beingserviced by a first cell, wherein the mobility data includes a vectorindicating a motion of the device and classifying the device based onthe mobility data to yield a classification, wherein, when making areselection or a handoff decision, a cell type for the classificationincludes at least one of a preferred cell type and at least one of anacceptable cell type.

In another example, the system can include a processor and acomputer-readable storage device storing instructions which, whenexecuted by the processor, cause the processor to perform operationsincluding receiving mobility data for a device being serviced by a firstcell in a heterogeneous network including at least a non-cellular nodetype and a cellular node type, and classifying the mobility data in avirtual layer for the device to yield a classification, wherein theclassification includes an access type and an indication of an accesspriority to the access type, wherein the access priority includes atleast an acceptable access type or a preferred access type, and whereinthe access type is identified from a list of access types which includesat least one of a non-cellular node type and a cellular node type. Theoperations further include measuring traffic characteristics in thevirtual layer from a prioritized list of traffic characteristics, thetraffic characteristics including, for a plurality of devices operatingwithin the heterogeneous network, the plurality of devices including thedevice and at least one additional device, two or more of (1) anadjustment of a node boundary of a node in the heterogenous network, (2)an adjustment of a power level of a node in the heterogenous network,(3) balancing of a cell load of a node in the heterogenous network, (4),a distribution of a load amongst nodes in the heterogenous network basedon communications with the plurality of devices, (5) a distribution ofthe plurality of devices in the heterogenous network, and (6) adistribution of the plurality of devices and a load level of respectivenodes in the heterogenous network, to yield a measurement and making ahandoff decision in the virtual layer when handing off the device fromthe first cell to a second cell based at least in part on theclassification and the measurement.

A separate example can include a non-transitory computer-readablestorage device storing instructions which, when executed by a computingdevice, cause the computing device to perform the operations describedabove.

The computer-readable storage device can store additional instructionswhich, when executed by the one or more processors, cause the one ormore processors to perform further operations including only choosing anon-preferred cell type when a received signal is stronger than a higherthreshold compared to a threshold required for the preferred cell type.The at least one of the preferred cell type can include at least onecell from a prioritized list of cells. The operations can includereceiving mobility data for a device being serviced by a first cell,wherein the mobility data includes a vector indicating a motion of thedevice and classifying the device based on the mobility data to yield aclassification, wherein, when making a reselection or a handoffdecision, a cell type for the classification can includes at least oneof a preferred cell type and at least one of an acceptable cell type.

The non-transitory computer-readable storage device can store additionalinstructions which, when executed by the one or more processors, causethe one or more processors to perform further operations including onlychoosing a non-preferred cell type when a received signal is strongerthan a higher threshold compared to a threshold required for thepreferred cell type. The at least one of the preferred cell type caninclude at least one cell from a prioritized list of cells.

The various examples described above are provided by way of illustrationonly and should not be construed to limit the scope of the disclosure.Various modifications and changes may be made to the principlesdescribed herein without following the example examples and applicationsillustrated and described herein, and without departing from the spiritand scope of the disclosure. Claim language reciting “at least one of” aset indicates that one member of the set or multiple members of the setsatisfy the claim.

We claim:
 1. A method comprising: receiving mobility data in a virtuallayer for a device being serviced by a first cell in a heterogeneousnetwork comprising at least a non-cellular node type and a cellular nodetype; classifying the mobility data in the virtual layer for the deviceto yield a classification, wherein the classification comprises anaccess type and an indication of an access priority to the access type,wherein the access priority comprises at least an acceptable access typeor a preferred access type, and wherein the access type is identifiedfrom a list of access types which comprises at least one of anon-cellular node type and a cellular node type; measuring trafficcharacteristics from a prioritized list of traffic characteristics, thetraffic characteristics comprising, for a plurality of devices operatingwithin the heterogeneous network, the plurality of devices comprisingthe device and at least one additional device, two or more of (1) anadjustment of a node boundary of a node in the heterogenous network, (2)an adjustment of a power level of a node in the heterogenous network,(3) balancing of a cell load of a node in the heterogenous network, (4),a distribution of a load amongst nodes in the heterogenous network basedon communications with the plurality of devices, (5) a distribution ofthe plurality of devices in the heterogenous network, and (6) adistribution of the plurality of devices and a load level of respectivenodes in the heterogenous network, to yield a measurement; and making ahandoff decision in the virtual layer when handing off the device fromthe first cell to a second cell based at least in part on theclassification and the measurement.
 2. The method of claim 1, whereinthe mobility data comprises a vector indicating a direction of motion ofthe device, and wherein the vector comprises a first dimensionassociated with time and a second dimension.
 3. The method of claim 1,wherein the classification is one of slow speed, medium speed and fastspeed.
 4. The method of claim 1, wherein a boundary that defines amobility class is one of fixed or variable and wherein when the boundaryis variable, the boundary is used to balance a load between cells. 5.The method of claim 1, wherein a mobility state comprises a rate ofmovement of the device.
 6. The method of claim 1, wherein the preferredaccess type for a slow speed classification comprises a micro cell typeand a small cell type, and an acceptable access type for the slow speedclassification comprises a large cell type.
 7. The method of claim 1,wherein the preferred access type of a medium speed classificationcomprises a small cell type, and the acceptable access type for themedium speed classification comprises a large cell type.
 8. The methodof claim 1, wherein the preferred access type of a fast classificationcomprises a large cell type, and the acceptable access type for the fastclassification comprises none.
 9. The method of claim 1, wherein thedevice only chooses a non-preferred cell type when a received signal isstronger than a higher threshold compared to a threshold required for apreferred cell type.
 10. The method of claim 9, wherein when making ahandoff decision, a reselection is triggered when the first cell nolonger provides a sufficient signal or when the first cell is of anon-preferred type, and wherein making the handoff decision compriseshanding off to the second cell when the second cell is one of thepreferred cell type or is suitable.
 11. The method of claim 1, whereinthe classification comprises a location of the first cell and a pathtraveled across the first cell.
 12. The method of claim 1, whereinmaking the handoff decision is further based at least in part on a firstspectral efficiency of the first cell and a second spectral efficiencyof the second cell.
 13. The method of claim 1, making the handoffdecision is further based at least in part on a bandwidth used by thedevice and an available bandwidth of the second cell.
 14. A systemcomprising: a processor; and a computer-readable storage device storinginstructions which, when executed by the processor, cause the processorto perform operations comprising: receiving mobility data for a devicebeing serviced by a first cell in a heterogeneous network comprising atleast a non-cellular node type and a cellular node type; classifying themobility data in a virtual layer for the device to yield aclassification, wherein the classification comprises an access type andan indication of an access priority to the access type, wherein theaccess priority comprises at least an acceptable access type or apreferred access type, and wherein the access type is identified from alist of access types which comprises at least one of a non-cellular nodetype and a cellular node type; measuring traffic characteristics in thevirtual layer from a prioritized list of traffic characteristics, thetraffic characteristics comprising, for a plurality of devices operatingwithin the heterogeneous network, the plurality of devices comprisingthe device and at least one additional device, two or more of (1) anadjustment of a node boundary of a node in the heterogenous network, (2)an adjustment of a power level of a node in the heterogenous network,(3) balancing of a cell load of a node in the heterogenous network, (4),a distribution of a load amongst nodes in the heterogenous network basedon communications with the plurality of devices, (5) a distribution ofthe plurality of devices in the heterogenous network, and (6) adistribution of the plurality of devices and a load level of respectivenodes in the heterogenous network, to yield a measurement; and making ahandoff decision in the virtual layer when handing off the device fromthe first cell to a second cell based at least in part on theclassification and the measurement.
 15. A non-transitorycomputer-readable storage device storing instructions which, whenexecuted by a computing device, cause the computing device to performfurther operations comprising: receiving mobility data in a virtuallayer for a device being serviced by a first cell in a heterogeneousnetwork comprising at least a non-cellular node type and a cellular nodetype; classifying the mobility data in the virtual layer for the deviceto yield a classification, wherein the classification comprises anaccess type and an indication of an access priority to the access type,wherein the access priority comprises at least an acceptable access typeor a preferred access type, and wherein the access type is identifiedfrom a list of access types which comprises at least one of anon-cellular node type and a cellular node type; measuring trafficcharacteristics from a prioritized list of traffic characteristics, thetraffic characteristics comprising, for a plurality of devices operatingwithin the heterogeneous network, the plurality of devices comprisingthe device and at least one additional device, two or more of (1) anadjustment of a node boundary of a node in the heterogenous network, (2)an adjustment of a power level of a node in the heterogenous network,(3) balancing of a cell load of a node in the heterogenous network, (4),a distribution of a load amongst nodes in the heterogenous network basedon communications with the plurality of devices, (5) a distribution ofthe plurality of devices in the heterogenous network, and (6) adistribution of the plurality of devices and a load level of respectivenodes in the heterogenous network, to yield a measurement; and making ahandoff decision in the virtual layer when handing off the device fromthe first cell to a second cell based at least in part on theclassification and the measurement.